What is it that has significantly changed the way we have been testing applications in recent years? The advent of DevOps and intelligent automation, along with the penetration of digital applications is why the current testing and QA paradigm is shifting rapidly. The delivery timelines have shrunk from months to weeks and these days, testing has shifted both left and right in the software development lifecycle, thanks to managed QA services.
The rise of DevOps and Agile have equated the development and testing environment into a single continuous activity. Testing as an operation has evolved to quality engineering which begins upfront as part of initial application planning and establishes a continuous feedback loop.
In order to completely understand the magnitude of evolution from testing to quality engineering, one needs to recognize how ‘data’ has changed software development itself from inside out. The potential of data surpasses beyond fueling automation use cases and AI learning datasets for repetitive development and testing tasks.
Users generate an astronomical amount of data every day. And this data is now helping quality engineers to predict risk, identify opportunities, increase speed and agility and minimize technical debt.
This huge current of data has made the quality engineer’s role far more exciting but also complex. And this will further evolve as we keep on moving towards AI, edge computing and the massive IoT end datasets which require machine-to-machine (M2M) communications with complete autonomy and failsafe protection.
In order to make sure that a business runs optimally and perennially these shifts in quality, technology, people, and organizations, Quality Engineering is on the verge of evolving into a pervasive, real-time, and insight-driven function, augmented by AI-led autonomous frameworks.
Across the most active five dimensions, i.e. data, frameworks, process, technology, and the organization, the process of testing has shifted away from the traditional approaches towards new ideas and new methodologies.
Data
The unsurmountable growth in data volumes and variety that are sourced from a huge number of modern enterprise channels and devices are experiencing a shift from ‘test data’ to ‘test insights’ through applied analytics.
Frameworks
The process of quality engineering is increasingly moving beyond script-driven approaches and more towards autonomous frameworks which aim at bringing developers, customers, and end-users together. This aligns the application quality with business needs.
Process
The main focus has shifted from issues-based resolution to real-time monitoring and integration, using intelligent identity management. This technological advantage has enabled the integration of any technology stack and creating a new paradigm that is more inclined towards quality.
Technology
Large enterprises and organizations are moving towards creating a pervasive AI layer across the organization to first augment testing professionals and then enable truly self-managing QE functions.This leads to embedding trust across the entire system which prevents unsolicited bias.
Organization
Inter-organizational roles are evolving and QA testing is moving outside the four walls of the Center of Excellence, as interconnected virtual teams consisting of a variety of industry, business, and technology expertise, put together around common business purposes, engineer radically different kinds of borderless enterprises.
How does the future hold?
To conclude, the method of applied analytics will be vital for QE and Managed QA services in the new. Enterprise applications and channels are growing in number and complexity. Along with it, new sources of test data are emerging from a multitude of connected devices, platforms and technologies. Hence, both, the volume and veracity of test data is increasing dramatically.
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